Meet the penguins

The palmerpenguins data contains size measurements for three penguin species observed on three islands in the Palmer Archipelago, Antarctica.

The Palmer Archipelago penguins. Artwork by @allison_horst.
The Palmer Archipelago penguins. Artwork by @allison_horst.


These data were collected from 2007 - 2009 by Dr. Kristen Gorman with the Palmer Station Long Term Ecological Research Program, part of the US Long Term Ecological Research Network. The data were imported directly from the Environmental Data Initiative (EDI) Data Portal, and are available for use by CC0 license (“No Rights Reserved”) in accordance with the Palmer Station Data Policy.

Installation

You can install the released version of palmerpenguins from CRAN with:

install.packages("palmerpenguins")

Or install the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("allisonhorst/palmerpenguins")

The palmerpenguins package

This package contains two datasets:

  1. Here, we’ll focus on a curated subset of the raw data in the package named penguins.

  2. The raw data, accessed from the Environmental Data Initiative (see full data citations below), is also available as palmerpenguins::penguins_raw.

The curated palmerpenguins::penguins dataset contains 8 variables (n = 344 penguins). You can read more about the variables by typing ?penguins.

glimpse(penguins)
#> Rows: 344
#> Columns: 8
#> $ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
#> $ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
#> $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
#> $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
#> $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
#> $ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
#> $ sex               <fct> male, female, female, NA, female, male, female, male…
#> $ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

The palmerpenguins::penguins data contains 333 complete cases, with 19 missing values.

Highlights

Exploring factors

The penguins data has three factor variables:

penguins %>%
  dplyr::select(where(is.factor)) %>% 
  glimpse()
#> Rows: 344
#> Columns: 3
#> $ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie…
#> $ island  <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Torgers…
#> $ sex     <fct> male, female, female, NA, female, male, female, male, NA, NA, …
# Count penguins for each species / island
penguins %>%
  count(species, island, .drop = FALSE)
#> # A tibble: 9 × 3
#>   species   island        n
#>   <fct>     <fct>     <int>
#> 1 Adelie    Biscoe       44
#> 2 Adelie    Dream        56
#> 3 Adelie    Torgersen    52
#> 4 Chinstrap Biscoe        0
#> 5 Chinstrap Dream        68
#> 6 Chinstrap Torgersen     0
#> 7 Gentoo    Biscoe      124
#> 8 Gentoo    Dream         0
#> 9 Gentoo    Torgersen     0
ggplot(penguins, aes(x = island, fill = species)) +
  geom_bar(alpha = 0.8) +
  scale_fill_manual(values = c("darkorange","purple","cyan4"), 
                    guide = FALSE) +
  theme_minimal() +
  facet_wrap(~species, ncol = 1) +
  coord_flip()

# Count penguins for each species / sex
penguins %>%
  count(species, sex, .drop = FALSE)
#> # A tibble: 8 × 3
#>   species   sex        n
#>   <fct>     <fct>  <int>
#> 1 Adelie    female    73
#> 2 Adelie    male      73
#> 3 Adelie    <NA>       6
#> 4 Chinstrap female    34
#> 5 Chinstrap male      34
#> 6 Gentoo    female    58
#> 7 Gentoo    male      61
#> 8 Gentoo    <NA>       5
ggplot(penguins, aes(x = sex, fill = species)) +
  geom_bar(alpha = 0.8) +
  scale_fill_manual(values = c("darkorange","purple","cyan4"), 
                    guide = FALSE) +
  theme_minimal() +
  facet_wrap(~species, ncol = 1) +
  coord_flip()

# Penguins are fun to summarize!
penguins %>% 
  count(species)
#> # A tibble: 3 × 2
#>   species       n
#>   <fct>     <int>
#> 1 Adelie      152
#> 2 Chinstrap    68
#> 3 Gentoo      124
penguins %>% 
  group_by(species) %>% 
  summarize(across(where(is.numeric), mean, na.rm = TRUE))
#> # A tibble: 3 × 6
#>   species   bill_length_mm bill_depth_mm flipper_length_mm body_mass_g  year
#>   <fct>              <dbl>         <dbl>             <dbl>       <dbl> <dbl>
#> 1 Adelie              38.8          18.3              190.       3701. 2008.
#> 2 Chinstrap           48.8          18.4              196.       3733. 2008.
#> 3 Gentoo              47.5          15.0              217.       5076. 2008.

Exploring scatterplots

penguins %>%
  dplyr::select(body_mass_g, ends_with("_mm")) %>% 
  glimpse()
#> Rows: 344
#> Columns: 4
#> $ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
#> $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
#> $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
#> $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
# Scatterplot example 1: penguin flipper length versus body mass
ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point(aes(color = species, 
                 shape = species),
             size = 2) +
  scale_color_manual(values = c("darkorange","darkorchid","cyan4")) 


# Scatterplot example 2: penguin bill length versus bill depth
ggplot(data = penguins, aes(x = bill_length_mm, y = bill_depth_mm)) +
  geom_point(aes(color = species, 
                 shape = species),
             size = 2)  +
  scale_color_manual(values = c("darkorange","darkorchid","cyan4"))

You can add color and/or shape aesthetics in ggplot2 to layer in factor levels like we did above. With three factor variables to work with, you can add another factor layer with facets, like the plot below.

ggplot(penguins, aes(x = flipper_length_mm,
                     y = body_mass_g)) +
  geom_point(aes(color = sex)) +
  scale_color_manual(values = c("darkorange","cyan4"), 
                     na.translate = FALSE) +
  facet_wrap(~species)

Bill dimensions

The culmen is the upper ridge of a bird’s bill. In the simplified penguins data, culmen length and depth are renamed as variables bill_length_mm and bill_depth_mm to be more intuitive.

For this penguin data, the culmen (bill) length and depth are measured as shown below (thanks Kristen Gorman for clarifying!):

Exploring distributions

# Jitter plot example: bill length by species
ggplot(data = penguins, aes(x = species, y = bill_length_mm)) +
  geom_jitter(aes(color = species),
              width = 0.1, 
              alpha = 0.7,
              show.legend = FALSE) +
  scale_color_manual(values = c("darkorange","darkorchid","cyan4"))


# Histogram example: flipper length by species
ggplot(data = penguins, aes(x = flipper_length_mm)) +
  geom_histogram(aes(fill = species), alpha = 0.5, position = "identity") +
  scale_fill_manual(values = c("darkorange","darkorchid","cyan4"))

References

Data originally published in:

Individual datasets:

Individual data can be accessed directly via the Environmental Data Initiative: